Bio
Efrem Bonfiglioli is Adjunct Professor at SAIS Europe
Professor Bonfiglioli is a seasoned model and AI risk management professional with a passion for advising stakeholders on best practices for effective model and AI system management in line with regulatory requirements globally. He has held various roles related to model and AI risk management across multiple lines of defense in leading global banking institutions, covering a wide range of model types and AI systems.
Efrem is a visiting professor at universities in Italy and the UK where he teaches courses ranging from foundational financial subjects to advanced (risk) quantitative modelling including data analysis and machine learning. He earned his PhD in Financial Mathematics, where he focused on researching the applications of jump-diffusion models in the context of derivatives pricing.
Courses
- Data Mining and Machine Learning
Nowadays datasets that have relevance for managerial decisions are accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, bar-code readers, and intelligent machines. Such datasets are often stored in data warehouses specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. Many successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, stock market investments, and so on. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods and Machine Learning algorithms that have emerged from both fields and proven to be of value in recognizing patterns and making predictions. We will survey applications and provide an opportunity for hands-on experimentation with Machine Learning algorithms for data mining using Python. Prerequisite: Statistics for Data Analysis.
Prerequisites: SA.100.501[C]
- Quantitative Approaches to Risk Assessment
Financial markets are environments where participants directly or indirectly operate based on different needs and preferences for risk. Regulators have the responsibility to make sure that all risks are identified, assessed and managed to avoid unnecessary concentrations. The purpose of this course is to provide students with an overview of the different types of risks involved in various market operations. They’ll learn how quantitative techniques can be applied to measure and mitigate these risks. The course begins with an overview of financial markets players and concrete use cases are provided to understand what their needs are. Some of the key risk factors will be presented together to examples of instruments that are available for risk management. Stress testing will be explored as a tool to inform decision making and to uncover unpredicted adverse circumstances. Numerical examples and assignments will be given to students to gain a deeper understanding on the topics analysed. Insights to the role of regulators to minimize systemic risk will be offered with specific reference to capital adequacy requirements.
